Constructing and comparing user mobility profiles for location-based services
Constructing and comparing user mobility profiles for location-based services
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DOI：
10.1145/2480362.2480418
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发表时间：
2013-03
期刊：
影响因子：
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通讯作者：
Xihui Chen;Jun Pang;Ran Xue
中科院分区：
文献类型：
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作者：
Xihui Chen;Jun Pang;Ran Xue
随着位置获取技术的不断发展，我们可以更好地访问大型时空数据集。这为基于位置的服务（LBS）带来了新的机会，特别是当用户的移动行为的知识（即，移动性简档）可以从这样的数据集中提取。例如，在社交网络中，可以根据用户移动性简档之间的相似性分数来推荐朋友。在本文中，我们提出了一种新的方法来构建用户的移动配置文件和计算用户之间的移动相似性。我们模型的移动配置文件的痕迹，用户经常访问的地方，并使用频繁的序列模式挖掘技术来提取它们。为了比较用户的移动配置文件，我们首先讨论了文献中的相似性度量的弱点，然后提出了我们的新的测量。我们使用微软亚洲研究院发布的真实数据集对我们的工作进行了评估，实验结果表明，我们的方法在不同方面都优于现有的工作。
With the increasing availability of location-acquisition technologies, we have better access to collections of large spatio-temporal datasets. This brings new opportunities to location-based services (LBS), especially when knowledge of users' movement behaviour (i.e., mobility profiles) can be extracted from such datasets. For instance, in social networks, friends can be recommended according to similarity scores between user mobility profiles. In this paper, we propose a new approach to construct users' mobility profiles and calculate the mobility similarities between users. We model mobility profiles as traces of places that users frequently visit and use frequent sequential pattern mining technologies to extract them. To compare users' mobility profiles, we first discuss the weakness of a similarity measurement in the literature and then propose our new measurement. We evaluate our work using a real-life dataset published by Microsoft Research Asia and the experimental results show that our approach outperforms the existing works on different aspects.